Assessing protein kinase target similarity: Comparing sequence, structure, and cheminformatics approaches.

In just over two decades, structure based protein kinase inhibitor discovery has grown from trial and error approaches, using individual target structures, to structure and data driven approaches that may aim to optimize inhibition properties across several targets. This is increasingly enabled by the growing availability of potent compounds and kinome-wide binding data. Assessing the prospects for adapting known compounds to new therapeutic uses is thus a key priority for current drug discovery efforts. Tools that can successfully link the diverse information regarding target sequence, structure, and ligand binding properties now accompany a transformation of protein kinase inhibitor research, away from single, block-buster drug models, and toward "personalized medicine" with niche applications and highly specialized research groups. Major hurdles for the transformation to data driven drug discovery include mismatches in data types, and disparities of methods and molecules used; at the core remains the problem that ligand binding energies cannot be predicted precisely from individual structures. However, there is a growing body of experimental data for increasingly successful focussing of efforts: focussed chemical libraries, drug repurposing, polypharmacological design, to name a few. Protein kinase target similarity is easily quantified by sequence, and its relevance to ligand design includes broad classification by key binding sites, evaluation of resistance mutations, and the use of surrogate proteins. Although structural evaluation offers more information, the flexibility of protein kinases, and differences between the crystal and physiological environments may make the use of crystal structures misleading when structures are considered individually. Cheminformatics may enable the "calibration" of sequence and crystal structure information, with statistical methods able to identify key correlates to activity but also here, "the devil is in the details." Examples from specific repurposing and polypharmacology applications illustrate these points. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases.

Experimental and computational studies on the inhibition of acetylcholinesterase by curcumin and some of its derivatives.

Recent studies have demonstrated several biological activities of curcumin with therapeutic potential against Alzheimer's disease, among them the inhibition of the enzyme acetylcholinesterase (AChE). Aiming at identifying the chemical features relevant for this activity, the inhibition of curcumin and a set of 7 derivatives against AChE of E. electricus was measured. These derivatives presented lower activity than curcumin, allowing for the identification of possible unfavorable enzyme-inhibitor interactions. Our computational approach was to dock the molecules to the active site of AChE, followed by an analysis of hydrogen bonds and close contacts to relevant aromatic amino acid residues. To account for inhibitory activity, we sought to define the common structural features between known acetylcholinesterase inhibitors and the tested derivatives. A pharmacophore model was generated, which consisted of two hydrophobic, one aromatic and one hydrogen bond acceptor features. We conclude that the presence of two aromatic rings and the distance between them, allows curcumin and its derivatives to favorably interact with both the quaternary and peripheral sites of AChE. Hydrogen bonds can be formed with the quaternary and acyl sites, which should further stabilize the complex. The acylation of the hydroxyl groups and the reduction of the conjugated double bonds lowered the inhibitory activity, pointing to the modification of the keto-enol moiety as the best alternative for the design of more potent curcumin derivatives as acetylcholinesterase inhibitors.